2020
DOI: 10.1371/journal.pone.0241917
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Localized prediction of tissue outcome in acute ischemic stroke patients using diffusion- and perfusion-weighted MRI datasets

Abstract: Background An accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop … Show more

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Cited by 8 publications
(3 citation statements)
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“…In our review, four articles ( Monteiro et al, 2018 ; Heo et al, 2019 ; Nishi et al, 2019 ; Li et al, 2022 ) used mRS as functional outcomes, and they all thought mRS ≤ 2 was a good outcome. The other three articles ( Lin et al, 2018 ; Nielsen et al, 2018 ; Grosser et al, 2020 ) used radiological biomarkers, follow-up lesion volume, and neurological deterioration as outcomes.…”
Section: Resultsmentioning
confidence: 99%
“…In our review, four articles ( Monteiro et al, 2018 ; Heo et al, 2019 ; Nishi et al, 2019 ; Li et al, 2022 ) used mRS as functional outcomes, and they all thought mRS ≤ 2 was a good outcome. The other three articles ( Lin et al, 2018 ; Nielsen et al, 2018 ; Grosser et al, 2020 ) used radiological biomarkers, follow-up lesion volume, and neurological deterioration as outcomes.…”
Section: Resultsmentioning
confidence: 99%
“…The mixed model significantly improved the effect size at the 0.01 level, including ROC AUC and Dice values. The mixed LR model had the highest average values for ROC AUC, Dice coefficient, sensitivity, and specificity, followed by local LR, mixed, and local RF models, as well as global LR and RF models ( 67 ).…”
Section: Progress In Predicting the Rehabilitation Of Ischemic Stroke...mentioning
confidence: 99%
“…In this study, peak signal-to-noise ratio (PSNR) [17] and structural similarity (SSIM) [18] are used as two parameters to evaluate image quality. PSNR can be defined by the maximum pixel value and mean square error between images, which can be expressed as follows:…”
Section: Image Quality Assessment Indicatorsmentioning
confidence: 99%